Ranking in Distributed Uncertain Database Environments

Distributed data processing is a major field in nowadays applications. Many applications collect and process data from distributed nodes to gain overall results. Large amount of data transfer and network delay made data processing in a centralized manner a hard operation representing an important problem. A very common way to solve this problem is ranking queries. Ranking or top-k queries concentrate only on the highest ranked tuples according to user's interest. Another issue in most nowadays applications is data uncertainty. Many techniques were introduced for modeling, managing, and processing uncertain databases. Although these techniques were efficient, they didn't deal with distributed data uncertainty. This paper deals with both data uncertainty and distribution based on ranking queries. A novel framework is proposed for ranking distributed uncertain data. The framework has a suite of novel algorithms for ranking data and monitoring updates. These algorithms help in reducing the communication rounds used and amount of data transmitted while achieving efficient and effective ranking. Experimental results show that the proposed framework has a great impact in reducing communication cost compared to other techniques.DOI:http://dx.doi.org/10.11591/ijece.v4i4.592

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

Finding Top-k Dominance on Incomplete Big Data Using Map-Reduce Framework

Incomplete data is one major kind of multi-dimensional dataset that has random-distributed missing nodes in its dimensions. It is very difficult to retrieve information from this type of dataset when it becomes huge. Finding top-k dominant values in this type of dataset is a challenging procedure. Some algorithms are present to enhance this process but are mostly efficient only when dealing with a small-size incomplete data. One of the algorithms that make the application of TKD query possible is the Bitmap Index Guided (BIG) algorithm. This algorithm strongly improves the performance for incomplete data, but it is not originally capable of finding top-k dominant values in incomplete big data, nor is it designed to do so. Several other algorithms have been proposed to find the TKD query, such as Skyband Based and Upper Bound Based algorithms, but their performance is also questionable. Algorithms developed previously were among the first attempts to apply TKD query on incomplete data; however, all these had weak performances or were not compatible with the incomplete data. This thesis proposes MapReduced Enhanced Bitmap Index Guided Algorithm (MRBIG) for dealing with the aforementioned issues. MRBIG uses the MapReduce framework to enhance the performance of applying top-k dominance queries on huge incomplete datasets. The proposed approach uses the MapReduce parallel computing approach using multiple computing nodes. The framework separates the tasks between several computing nodes that independently and simultaneously work to find the result. This method has achieved up to two times faster processing time in finding the TKD query result in comparison to previously presented algorithms

Top-k aggregation queries in large-scale distributed systems

Distributed top-k query processing has recently become an essential functionality in a large number of emerging application classes like Internet traffic monitoring and Peer-to-Peer Web search. This work addresses efficient algorithms for distributed top-k queries in wide-area networks where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers. More precisely, in this thesis, we make the following distributions: We present the family of KLEE algorithms that are a fundamental building-block towards efficient top-k query processing in distributed systems. We present means to model score distributions and show how these score models can be used to reason about parameter values that play an important role in the overall performance of KLEE. We present GRASS, a family of novel algorithms based on three optimization techniques significantly increased overall performance of KLEE and related algorithms. We present probabilistic guarantees for the result quality. Moreover, we present Minerva1, a distributed search engine. Minerva offers a highly distributed (in both the data dimension and the computational dimension), scalable, and efficient solution toward the development of internet-scale search engines.Top-k Anfragen spielen eine große Rolle in einer Vielzahl von Anwendungen, insbesondere im Bereich von Informationssystemen, bei denen eine kleine, sorgfältig ausgewählte Teilmenge der Ergebnisse den Benutzern präsentiert werden soll. Beispiele hierfür sind Suchmaschinen wie Google, Yahoo oder MSN. Obwohl die Forschung in diesem Bereich in den letzten Jahren große Fortschritte gemacht hat, haben Top-k-Anfragen in verteilten Systemen, bei denen die Daten auf verschiedenen Rechnern verteilt sind, vergleichsweise wenig Aufmerksamkeit erlangt. In dieser Arbeit beschäftigen wir uns mit der effizienten Verarbeitung eben dieser Anfragen. Die Hauptbeiträge gliedern sich wie folgt. Wir präsentieren KLEE, eine Familie neuartiger Top-k-Algorithmen. Wir entwickeln Modelle mit denen Datenverteilungen beschrieben werden können. Diese Modelle sind die Grundlage für eine Schätzung diverser Parameter, die einen großen Einfluss auf die Performanz von KLEE und anderen ähnlichen Algorithmen haben. Wir präsentieren GRASS, eine Familie von Algorithmen, basierend auf drei neuartigen Optimierungstechniken, mit denen die Performanz von KLEE und ähnlichen Algorithmen verbessert wird. Wir präsentieren probabilistische Garantien für die Ergebnisgüte. Wir präsentieren Minerva, eine neuartige verteilte Peer-to-Peer-Suchmaschine

Efficient Distance Join Query Processing in Distributed Spatial Data Management Systems

Due to the ubiquitous use of spatial data applications and the large amounts of such data these applications use, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Distance Join Queries (DJQs) are important and frequently used operations in numerous applications, including data mining, multimedia and spatial databases. DJQs (e.g., k Nearest Neighbor Join Query, k Closest Pair Query, ε Distance Join Query, etc.) are costly operations, since they involve both the join and distance-based search, and performing DJQs efficiently is a challenging task. Recent Big Data developments have motivated the emergence of novel technologies for distributed processing of large-scale spatial data in clusters of computers, leading to Distributed Spatial Data Management Systems (DSDMSs). Distributed cluster-based computing systems can be classified as Hadoop-based or Spark-based systems. Based on this classification, in this paper, we compare two of the most recent and leading DSDMSs, SpatialHadoop and LocationSpark, by evaluating the performance of several existing and newly proposed parallel and distributed DJQ algorithms under various settings with large spatial real-world datasets. A general conclusion arising from the execution of the distributed DJQ algorithms studied is that, while SpatialHadoop is a robust and efficient system when large spatial datasets are joined (since it is built on top of the mature Hadoop platform), LocationSpark is the clear winner in total execution time efficiency when medium spatial datasets are combined (due to in-memory processing provided by Spark). However, LocationSpark requires higher memory allocation when large spatial datasets are involved in DJQs (even more so when k and ε are large). Finally, this detailed performance study has demonstrated that the new distributed DJQ algorithms we have proposed are efficient, robust and scalable with respect to different parameters, such as dataset sizes, k, ε and number of computing nodes

Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom